Desert sand concrete construction monitoring method based on data analysis

Abstract

The invention discloses a desert sand concrete construction monitoring method based on data analysis, and relates to the technical field of concrete construction, and the technical scheme is characterized by comprising the following steps: collecting attribute characteristic information of desert sand concrete of a target construction project, screening out a first reference construction project set and a second reference construction project from historical construction projects of the same category according to the attribute feature information; concrete surface defect distribution structures in the construction process of the first reference construction project set are extracted, and defect distribution development trend processing is carried out on different concrete surface defect distribution structures to obtain defect distribution optimization trend features; extracting defect repair management information of the second reference construction project in a defect repair process; the method has the advantages that the defect distribution condition of the target project is obtained by monitoring the attribute feature information, and the desert sand concrete construction quality is effectively managed and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete construction, and more particularly to a desert sand concrete construction monitoring method based on data analysis. Background Art

[0002] As infrastructure construction continues to advance, demand for construction sand is increasing. Traditional river sand resources are facing depletion due to overexploitation, and increasingly stringent ecological protection requirements are limiting their extraction. In this context, desert sand is attracting attention as a potential sand resource. Its abundant reserves and widespread distribution mean that its rational development and utilization are expected to alleviate the imbalance between supply and demand for construction sand.

[0003] Traditional concrete construction monitoring methods struggle to effectively address the unique challenges of desert sand concrete. Previously, they relied heavily on empirical judgment and simple sampling tests, lacking in-depth analysis of the complex properties of desert sand and the interplay of multiple factors. Traditional methods can only detect defects such as cracks, holes, and insufficient strength in desert sand concrete after construction, making it difficult to accurately predict and prevent them before construction begins. This leads to high repair costs, delays, and compromised project quality and profitability. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a desert sand concrete construction monitoring method based on data analysis.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for monitoring desert sand concrete construction based on data analysis, the method comprising the following steps:

[0007] Collecting attribute characteristic information of desert sand concrete of the target construction project, and screening a first reference construction project set and a second reference construction project from historical construction projects of the same category based on the attribute characteristic information;

[0008] Extract the concrete surface defect distribution structure during the construction process of the first reference construction project set, and process the defect distribution development trend of different concrete surface defect distribution structures to obtain the defect distribution optimization trend characteristics;

[0009] Extracting defect repair management information of the second reference construction project during the defect repair process, comparing and statistically analyzing the repair management development trends of different defect repair management information to obtain repair management optimization trend characteristics;

[0010] A defect distribution monitoring result of the target construction project is obtained by performing a comprehensive evaluation based on the defect distribution optimization trend characteristics of the first environmental change data set and the first reference construction project set;

[0011] A comprehensive evaluation is conducted based on the second environmental change data set and the repair management optimization trend characteristics of the second reference construction project to obtain the defect repair management results of the target construction project.

[0012] The demand monitoring information of desert sand concrete of the target construction project is collected, and the demand monitoring information is input into the defect distribution prediction model and the repair management prediction model to obtain the monitoring results.

[0013] Preferably, the first environmental change data set and the second environmental change data set are obtained by processing and analyzing the environmental impacts of the first reference construction project set, the second reference construction project, and the target construction project, which specifically includes the following steps:

[0014] Performing an environmental impact assessment on the first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on the second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on the target construction project to obtain a target environmental correlation impact set;

[0015] The target environment association impact degree set and the first environment association impact degree set are changed by calculating the change ratio value to obtain the first environment change data set, and the target environment association impact degree set and the second environment association impact degree set are changed by calculating the change ratio value to obtain the second environment change data set.

[0016] Preferably, the attribute characteristic information includes desert sand content, desert sand strength grade, and construction process type.

[0017] Preferably, selecting the first reference construction project set and the second reference construction project from historical construction projects of the same category according to attribute feature information specifically includes the following steps:

[0018] Collect at least two historical construction projects with the same construction environment and the same category based on attribute feature information;

[0019] Statistically analyzing the concrete surface defect distribution results of each historical construction project to obtain a defect distribution statistical result set, and marking the corresponding historical construction project with the largest number of identical defect distribution conditions in the defect distribution statistical result set as a first reference construction project set;

[0020] Statistically analyzing the optimization results of the centralized defect repair management of the first reference construction project to obtain a repair management statistical result set;

[0021] The historical construction project corresponding to the best defect repair management optimization result in the repair management statistical result set is marked as the second reference construction project.

[0022] Preferably, the defect distribution development trend of different concrete surface defect distribution structures is processed to obtain defect distribution optimization trend characteristics, which specifically includes the following steps:

[0023] Extracting defect detection status data of one concrete surface defect distribution structure and marking it as a first defect status detection value, extracting defect detection status data of another concrete surface defect distribution structure and marking it as a second defect status detection value;

[0024] Calculating the difference between the first defect condition detection value and the second defect condition detection value to obtain a defect detection difference;

[0025] A difference defect distribution structure is obtained by extracting the difference between one concrete surface defect distribution structure and another concrete surface defect distribution structure;

[0026] Among them, the difference defect distribution structure and the defect detection difference are combined to form the defect distribution optimization trend feature.

[0027] Preferably, after comparing and statistically analyzing the repair management development trends of different defect repair management information, repair management optimization trend characteristics are obtained, which specifically includes the following steps:

[0028] Extracting defect repair completion time data of one type of defect repair management information and marking it as a first defect repair completion value; extracting defect repair completion time data of another type of defect repair management information and marking it as a second defect repair completion value;

[0029] Calculate the difference between the first defect repair completion value and the second defect repair completion value to obtain a defect repair time difference;

[0030] marking different repair processes for defects of the same type in one defect repair management information and another defect repair management information as different repair processes;

[0031] Among them, the defect repair time difference and the difference repair process mark are repair management optimization trend features.

[0032] Preferably, performing an environmental impact assessment on a first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on a second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on a target construction project to obtain a target environmental correlation impact set specifically include the following steps:

[0033] Obtaining environmental impact characteristic information for the first reference construction project set, the second reference construction project, and the target construction project; obtaining value indicators of various monitoring indicators in the first reference construction project set, the second reference construction project, and the target construction project; wherein the environmental impact characteristic information includes ambient temperature, humidity, and wind speed, and the value indicators include the curing speed of desert sand concrete;

[0034] After evaluating the correlation impact degree between the environmental impact characteristic information and the value indicators in the first reference construction project set, a first environmental correlation impact degree set is obtained;

[0035] After evaluating the correlation impact degree between the environmental impact characteristic information and the value index in the second reference construction project, a second environmental correlation impact degree set is obtained;

[0036] After evaluating the correlation impact degree between the environmental impact characteristic information and value indicators in the target construction project, the target environmental correlation impact degree set is obtained.

[0037] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for monitoring desert sand concrete construction based on data analysis is implemented.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] In this method, the first reference construction project set is screened by collecting attribute characteristic information of desert sand concrete from the target construction project. Its defect distribution optimization trend characteristics are extracted and combined with the first environmental change data set for comprehensive evaluation to obtain the target project's defect distribution. This enables the construction party to take targeted preventive measures before or during construction. Based on the repair management optimization trend characteristics of the second reference construction project and the second environmental change data set, a comprehensive evaluation of the target construction project's defect repair management is conducted to obtain defect repair management results. The most appropriate repair process is recommended for different types of defects, and the repair time and risk of recurrence of the defect are determined.

[0040] This application collects the attribute characteristic information of desert sand concrete of the target construction project, selects the historical project with the largest number of identical defect distribution situations in the defect distribution statistical result set from historical projects of the same construction environment and category as the first reference construction project set, extracts its defect distribution optimization trend characteristics, and establishes a defect distribution prediction model by combining the environmental change data sets of the target project and the reference project. At the same time, the target project demand monitoring information is collected as an input model, and the defect distribution situation of the target project is obtained by monitoring the attribute characteristic information, thereby achieving effective control over the construction quality of desert sand concrete and reducing quality risks and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a method for monitoring desert sand concrete construction based on data analysis proposed by the present invention;

[0042] Figure 2 A schematic diagram of the steps for obtaining defect distribution optimization trend characteristics in a desert sand concrete construction monitoring method based on data analysis proposed by the present invention;

[0043] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] Reference Figures 1 to 3 shown.

[0045] The embodiment further illustrates a method for monitoring desert sand concrete construction based on data analysis proposed by the present invention.

[0046] A method for monitoring desert sand concrete construction based on data analysis, the method comprising the following steps:

[0047] Collecting attribute characteristic information of desert sand concrete of the target construction project, and screening a first reference construction project set and a second reference construction project from historical construction projects of the same category based on the attribute characteristic information;

[0048] For example, in bridge construction or in the foundation and main structure of house construction, etc. Taking bridge construction as an example, whether it is a beam bridge, an arch bridge or a cable-stayed bridge, if desert sand concrete is used for the substructure (piers, abutments) or superstructure (beam) construction, they belong to the same category.

[0049] Extract the concrete surface defect distribution structure during the construction process of the first reference construction project set, and process the defect distribution development trend of different concrete surface defect distribution structures to obtain the defect distribution optimization trend characteristics;

[0050] Extracting defect repair management information of the second reference construction project during the defect repair process, comparing and statistically analyzing the repair management development trends of different defect repair management information to obtain repair management optimization trend characteristics;

[0051] After processing and analyzing the environmental impacts of the first reference construction project set, the second reference construction project, and the target construction project, a first environmental change data set and a second environmental change data set are obtained;

[0052] A defect distribution monitoring result of the target construction project is obtained by performing a comprehensive evaluation based on the defect distribution optimization trend characteristics of the first environmental change data set and the first reference construction project set;

[0053] A comprehensive evaluation is conducted based on the second environmental change data set and the repair management optimization trend characteristics of the second reference construction project to obtain the defect repair management results of the target construction project.

[0054] This application collects characteristic information about the properties of desert sand concrete for the target construction project (desert sand content, strength grade, construction process type, etc.). Based on this information, it selects historical construction projects of the same construction environment and category. By statistically analyzing the distribution of concrete surface defects and the optimization results of defect repair management in these historical projects, the historical projects corresponding to the best results are marked as the first reference construction project set and the second reference construction project, respectively.

[0055] The defect distribution structures of different concrete surfaces are extracted from the first reference construction project set. The defect detection status data of different structures (such as the detection values ​​of crack length and width of different structures) are calculated by difference. At the same time, the differences between structures (such as differences in steel bar arrangement, vibration method, etc.) are extracted and combined to form the defect distribution optimization trend characteristics, which are used to characterize the development trend of the defect distribution.

[0056] For the second reference construction project, the defect repair completion time data in different defect repair management information are extracted to perform difference calculation, and different repair processes for the same type of defects are marked to form the repair management optimization trend characteristics, reflecting the development trend of repair management.

[0057] Environmental impact assessments were conducted for the first reference construction project set, the second reference construction project, and the target construction project. Environmental impact characteristic information (ambient temperature, humidity, and wind speed) and value indicators (desert sand concrete curing speed) were obtained to assess the correlation between the two and generate corresponding impact sets. The first and second environmental change datasets were then generated by calculating the change ratios between the target environmental correlation impact set and the first and second environmental correlation impact sets, reflecting the dynamic changes in environmental factors.

[0058] After obtaining environmental impact characteristic information (such as ambient temperature, humidity, and wind speed) and value indicators (such as the curing rate of desert sand concrete) for the target construction project and the first reference construction project, the degree of environmental correlation between the two was assessed. By calculating the change ratio and other methods, a first environmental change dataset was generated, which reflects the changes in environmental factors affecting the target project compared to the first reference project.

[0059] Defect detection data (such as crack length, width, and number) was extracted from different concrete surface defect distribution structures within the first reference construction project set. Through interpolation calculation and structural difference extraction, defect distribution optimization trend characteristics were derived. These characteristics reflect the regularity and changing trends of defect distribution within the first reference project set. A comprehensive evaluation was conducted based on the first environmental change dataset and the defect distribution optimization trend characteristics of the projects within the first reference construction project set to obtain defect distribution monitoring results for the target construction project.

[0060] After obtaining the environmental impact characteristic information and value indicators of the target construction project and the second reference construction project, the degree of environmental correlation impact is evaluated, and the change ratio is calculated to obtain the second environmental change data set, which shows the changes in the environmental factor impact of the target project relative to the second reference project.

[0061] Different defect repair management information (such as defect repair completion time and repair process) was extracted from the second reference construction project. Through difference calculation (defect repair time difference) and process difference marking (difference repair process), repair management optimization trend characteristics were obtained. This characteristic reflects the characteristics and advantages of the second reference project in defect repair management. A comprehensive evaluation was conducted based on the second environmental change dataset and the repair management optimization trend characteristics of the second reference construction project to obtain the defect repair management results of the target construction project.

[0062] After processing and analyzing the environmental impacts of the first reference construction project set, the second reference construction project, and the target construction project, a first environmental change data set and a second environmental change data set are obtained, specifically comprising the following steps:

[0063] Performing an environmental impact assessment on the first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on the second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on the target construction project to obtain a target environmental correlation impact set;

[0064] The target environment association impact degree set and the first environment association impact degree set are changed by calculating the change ratio value to obtain the first environment change data set, and the target environment association impact degree set and the second environment association impact degree set are changed by calculating the change ratio value to obtain the second environment change data set.

[0065] Environmental impact characteristic information (such as ambient temperature, humidity, and wind speed) and value indicators of various monitoring indicators (such as the curing rate of desert sand concrete) were collected for the first reference construction project set, the second reference construction project, and the target construction project. Different environmental factors have different effects on concrete construction, such as temperature affecting the hydration reaction rate of concrete, and humidity affecting its drying shrinkage.

[0066] Assess the degree of correlation between environmental impact characteristics and value indicators for each project. For example, analyze the relationship between temperature changes and the curing rate of desert sand concrete to determine how the curing rate changes when the temperature increases or decreases by a certain value. This method generates the first environmental correlation impact degree set, the second environmental correlation impact degree set, and the target environmental correlation impact degree set.

[0067] Calculate the change ratio between the target environmental impact set and the first environmental impact set. Specifically, compare the differences in the impact of environmental factors on construction indicators between the target project and the first reference project and calculate the corresponding proportional change. For example, if a 1°C temperature increase in the first reference project increases the concrete curing speed by 2%, while a 1°C temperature increase in the target project increases the curing speed by 3%, the change ratio between the two can be calculated. This calculation yields the first environmental change dataset. Similarly, perform a similar calculation for the target environmental impact set and the second environmental impact set to yield the second environmental change dataset.

[0068] These two environmental change datasets reflect the dynamic changes in the environmental impact of the target construction project relative to the first and second reference construction projects. This data will be used to subsequently develop defect distribution and remediation management prediction models. These models will account for the environmental differences between the current project and the reference projects, thereby predicting the target project's defect distribution and remediation management needs.

[0069] Attribute characteristic information includes desert sand content, desert sand strength grade, and construction process type.

[0070] For example, by using near-infrared spectroscopy to scan concrete samples, different substances have unique absorption characteristics in the near-infrared spectral region. There are differences in the spectral absorption of desert sand and other concrete raw materials. By comparing with the spectral database of standard samples, the spectral data of the concrete sample to be tested can be obtained, and the desert sand content can be obtained through the spectral data.

[0071] If a uniaxial compressive strength test is used, the desert sand sample is tested on a press to obtain its uniaxial compressive strength and the strength grade of the desert sand is obtained.

[0072] Multiple surveillance cameras are installed at the construction site to monitor the entire concrete construction process. Image recognition technology is used to identify and analyze construction equipment and operating behaviors in the video footage. For example, the operating status of the concrete pump truck and the movement trajectory of the placing boom can be used to determine whether a pumping construction method is being used. The insertion depth and movement pattern of the vibrating rod can be used to determine the type of vibrating process.

[0073] The method of selecting a first reference construction project set and a second reference construction project set from historical construction projects of the same category according to attribute feature information specifically includes the following steps:

[0074] Collect at least two historical construction projects with the same construction environment and the same category based on attribute feature information;

[0075] Statistically analyzing the concrete surface defect distribution results of each historical construction project to obtain a defect distribution statistical result set, and marking the corresponding historical construction project with the largest number of identical defect distribution conditions in the defect distribution statistical result set as a first reference construction project set;

[0076] Statistically analyzing the optimization results of the centralized defect repair management of the first reference construction project to obtain a repair management statistical result set;

[0077] The historical construction project corresponding to the best defect repair management optimization result in the repair management statistical result set is marked as the second reference construction project.

[0078] This application searches for historical projects with the same construction environment (e.g., climate, site conditions) and category in a historical construction project database or industry project information repository based on the target project's desert sand concrete properties (desert sand content, strength grade, and construction process). For example, if the target project involves building a bridge foundation in a desert region, the application will select historical projects also located in a desert region and also involved bridge foundation construction.

[0079] Data on concrete surface defects (cracks, honeycombs, and pitting) was collected from multiple historical projects, including defect location, number, and severity. Defect density (number of defects per unit area) and a comprehensive defect severity score (weighted by indicators such as crack width and length) were calculated to obtain the concrete surface defect distribution results for each project, forming a defect distribution statistical result set.

[0080] After counting the defect distribution patterns across all historical projects, identify the group with the highest number of identical defect distribution patterns. Label this group of historical construction projects with identical defect distribution patterns as the first reference construction project set. This group of projects is chosen as the first reference construction project set because it represents a common defect distribution pattern across historical projects. Subsequent extraction of defect distribution optimization trend features based on this group can reveal patterns and trends in common defect distribution patterns.

[0081] Defect repair management information was collected for each construction project in the first reference construction project set, including repair solutions (grouting, repair material type, etc.), repair time, repair cost, and post-repair quality. An evaluation index system was established to quantitatively assess the defect repair management work of each project using indicators such as repair cost, comprehensive repair effect score (low probability of defect recurrence after repair, high score for good strength recovery), and repair workload per unit time, resulting in a repair management statistical result set.

[0082] The historical construction project with the best defect repair management optimization results (e.g., low repair cost, good results, and short repair time) is marked as the second reference construction project. This project has advantages in defect repair management, and its repair strategies and measures can provide a reference for the repair management prediction of the target project.

[0083] After processing the defect distribution development trend of different concrete surface defect distribution structures, the defect distribution optimization trend characteristics are obtained, which specifically includes the following steps:

[0084] Extracting defect detection status data of one concrete surface defect distribution structure and marking it as a first defect status detection value, extracting defect detection status data of another concrete surface defect distribution structure and marking it as a second defect status detection value;

[0085] Calculating the difference between the first defect condition detection value and the second defect condition detection value to obtain a defect detection difference;

[0086] A difference defect distribution structure is obtained by extracting the difference between one concrete surface defect distribution structure and another concrete surface defect distribution structure;

[0087] Among them, the difference defect distribution structure and the defect detection difference are combined to form the defect distribution optimization trend feature.

[0088] This application selects at least two different concrete surface defect distribution structures from a first reference construction project set, for example, one with cracks concentrated at the component edge and another with cracks dispersed across the component surface. For each structure, data reflecting the defect condition, such as the length, width, and number of cracks, and the area and depth of the honeycombed surface, are extracted and recorded as the first defect condition detection value and the second defect condition detection value, respectively.

[0089] Perform a numerical calculation on the first and second defect condition detection values ​​to determine the difference. For example, if the average crack length for the first structure is 5mm and the average crack length for the second structure is 3mm, the difference is 2mm. This calculation can intuitively demonstrate the differences in defect severity or quantity between different defect distribution structures.

[0090] By identifying factors such as rebar arrangement, pouring sequence, and vibration method that cause these differences, we can obtain a differential defect distribution structure. Combining the differential defect distribution structure with the defect detection difference creates a defect distribution optimization trend feature. For example, "because the rebar is densely arranged on the outside and sparse on the inside (differential defect distribution structure), the average length of the cracks on the outside is 2mm longer than that on the inside (defect detection difference)." This feature comprehensively reflects the differences in different defect distribution structures and the quantitative differences in defect conditions, providing a key basis for predicting the development trend of concrete surface defect distribution in target construction projects.

[0091] Comparing and statistically analyzing the repair management development trends of different defect repair management information, we can obtain the repair management optimization trend characteristics, which specifically includes the following steps:

[0092] Extracting defect repair completion time data of one type of defect repair management information and marking it as a first defect repair completion value; extracting defect repair completion time data of another type of defect repair management information and marking it as a second defect repair completion value;

[0093] Calculate the difference between the first defect repair completion value and the second defect repair completion value to obtain a defect repair time difference;

[0094] marking different repair processes for defects of the same type in one defect repair management information and another defect repair management information as different repair processes;

[0095] Among them, the defect repair time difference and the difference repair process are marked as repair management optimization trend features.

[0096] This application selects at least two different types of defect repair management information from the second reference construction project. For example, one is a repair solution for concrete surface cracks using epoxy resin grouting, and the other is a repair solution using cement mortar. For each repair solution, defect repair completion time data is extracted and marked as a first defect repair completion value and a second defect repair completion value.

[0097] Calculate the difference between the first defect repair completion time and the second defect repair completion time. For example, if the first repair solution (epoxy grouting) takes 2 days to complete, and the second solution (cement mortar application) takes 3 days, then the defect repair time difference is -1 day (indicating that the first solution is 1 day faster than the second solution). By calculating the time difference, you can intuitively compare the differences in repair efficiency between different repair solutions.

[0098] Compare two defect repair management records to identify different repair processes for the same defect type (e.g., both are cracks). For example, epoxy grouting may involve the use of pressure injection equipment, while cement mortar application relies primarily on manual application. Label these different repair processes as differential repair processes.

[0099] Combining the defect repair time difference and the different repair processes creates a trend characteristic for optimizing repair management. For example, "For crack defects, the epoxy resin grouting process is one day faster than the cement mortar application process (defect repair time difference), and pressure injection equipment is used (differential repair process)." This characteristic comprehensively reflects the differences in efficiency and process between different repair solutions, providing an important reference for defect repair management in target construction projects.

[0100] Performing an environmental impact assessment on a first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on a second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on a target construction project to obtain a target environmental correlation impact set specifically includes the following steps:

[0101] Obtain environmental impact characteristic information for the first reference construction project set, the second reference construction project, and the target construction project; obtain value indicators of various monitoring indicators in the first reference construction project set, the second reference construction project, and the target construction project; wherein the environmental impact characteristic information includes ambient temperature, humidity, and wind speed, and the value indicators include the curing speed of desert sand concrete;

[0102] After evaluating the correlation impact degree between the environmental impact characteristic information and the value indicators in the first reference construction project set, a first environmental correlation impact degree set is obtained;

[0103] After evaluating the correlation impact degree between the environmental impact characteristic information and the value index in the second reference construction project, a second environmental correlation impact degree set is obtained;

[0104] After evaluating the correlation impact degree between the environmental impact characteristic information and value indicators in the target construction project, the target environmental correlation impact degree set is obtained.

[0105] This application uses temperature sensors, humidity sensors, and anemometers to monitor and record temperature, humidity, wind speed, and other data in the construction environment in real time. This data reflects the external environmental conditions of the project. Different environmental conditions will have different impacts on desert sand concrete construction. For example, high temperatures may accelerate the evaporation of moisture in concrete, causing cracking; high humidity may slow the concrete curing process.

[0106] Taking the curing speed of desert sand concrete as an example, multiple sets of concrete specimens were prepared according to standards during the concrete pouring process for each project. Under specified curing conditions and in accordance with relevant specifications (such as the "Standard for Test Methods for Mechanical Properties of Ordinary Concrete"), the specimens were subjected to compressive strength tests at different ages (such as 1 day, 3 days, 7 days, and 28 days). The curing speed of desert sand concrete was determined by comparing the strength growth at different ages.

[0107] For example, determining that a 5°C increase in temperature increases concrete curing speed by 3% or determining the quantitative impact on concrete surface smoothness when wind speed reaches a certain threshold can be used. These impact level values ​​are organized and summarized to form a first-environment-related impact level set, a second-environment-related impact level set, and a target-environment-related impact level set.

[0108] The first reference construction project collection integrates different defect distribution structures (such as those caused by different reinforcement arrangements and casting processes) and defect detection differences (such as differences in crack length and number among different defect distribution structures). These characteristics reflect the inherent patterns and changing trends in defect distribution within historical projects.

[0109] The first change dataset is generated by calculating the change ratio between the target construction project and the first reference construction project set. This dataset reflects the dynamic changes in the impact of environmental factors (such as temperature, humidity, and wind speed) on key construction indicators (such as the curing speed of desert sand concrete) compared to the first reference project.

[0110] Based on the defect distribution optimization trend characteristics of the first environmental change data set and the first reference construction project set, a comprehensive evaluation is performed to obtain the defect distribution monitoring results of the target construction project, specifically:

[0111] Suppose the first environmental change dataset contains data on differences in environmental factors such as temperature, humidity, and wind speed between the target construction project and the first reference construction project. For example, the average temperature of the target project is 5°C higher than that of the first reference project, the humidity is 10% lower, and the wind speed is 2 m / s higher. These changes in environmental factors will affect the performance of desert sand concrete. Increased temperature may accelerate the hydration reaction of concrete, leading to faster early strength growth, but it may also cause cracking due to rapid water evaporation. Reduced humidity reduces the moist curing conditions of concrete, increasing the risk of shrinkage cracking. Increased wind speed further promotes water evaporation, affecting the surface smoothness of the concrete.

[0112] Defect distribution optimization trend features were extracted from the first reference construction project. For example, in the first reference project, when the temperature was high and the humidity was low during concrete pouring, the probability of cracks on the concrete surface increased. These cracks were mostly concentrated in the tensile zone of the concrete component, with crack widths ranging from 0.1 to 0.3 mm. Furthermore, it was found that honeycombing defects were more likely to occur when the desert sand had a high mud content, and were mostly distributed on the sides of the concrete component.

[0113] The first environmental change dataset and the defect distribution optimization trend characteristics of the first reference construction project are combined. An established evaluation model (e.g., a neural network-based evaluation model) is used with the changing values ​​of environmental factors and defect distribution characteristics as input parameters. After training, the model has learned the relationship between environmental factors and defect distribution. For example, based on a 5°C temperature increase and a 10% humidity decrease in the target project, as well as the correlation between high temperature and low humidity and cracks in the first reference project, the model predicts an 80% probability of surface cracks appearing on the concrete surface of the target project, with cracks between 0.1 and 0.3 mm wide appearing in the tensile zone of the component. Furthermore, considering the possible desert sand mud content in the target project (assuming it is similar to the first reference project), a 60% probability of honeycombing defects on the side of the component is predicted. Ultimately, defect distribution monitoring results for the target construction project are obtained, providing a basis for the construction company to take preventive measures (such as strengthening moisture retention and strictly controlling the desert sand mud content) in advance.

[0114] Based on the second environmental change dataset and the repair management optimization trend characteristics of the second reference construction project, a comprehensive evaluation was conducted to obtain the defect repair management results of the target construction project, specifically:

[0115] The second environmental change dataset records the differences in environmental factors between the target construction project and the second reference construction project. For example, the target project has a 10°C greater diurnal temperature difference than the second reference project, and fewer days with precipitation. This large diurnal temperature difference causes concrete to expand and contract more rapidly over time, increasing the risk of re-cracking after crack repair. Fewer days with precipitation indicate relatively poor natural curing conditions, potentially affecting the curing effectiveness of the repair material.

[0116] Characteristics of repair management optimization trends were extracted from the second reference construction project. For example, in the second reference project, when epoxy resin grouting was used to repair cracks with a width of 0.1-0.3 mm, the average repair time under relatively stable temperature conditions was two days, with a 10% probability of recurrence. However, when cement mortar was used to repair cracks with a width of 0.1-0.3 mm, the average repair time was three days under the same temperature conditions, with a 20% probability of recurrence.

[0117] An evaluation was conducted by combining the second environmental change dataset and the repair management optimization trend characteristics of the second reference construction project. An evaluation model (such as a decision tree-based evaluation model) was used to take the differences in environmental factors and repair management characteristics as input. Taking into account the large temperature difference between day and night in the target project, for cracks with a width of 0.1-0.3mm, the model evaluated based on the second reference project that the probability of cracks reappearing after repair using the epoxy resin grouting method increased to 15%, while the probability of cracks reappearing after repair using the cement mortar smearing method increased to 25%. At the same time, considering the impact of the lack of precipitation on the curing of the repair material, the evaluation showed that the repair completion time of the two repair methods was extended to 3 days and 4 days respectively. Finally, based on these evaluation results, the target construction project was given recommended repair processes for different types of defects (such as giving priority to the epoxy resin grouting method), estimated repair time, and the risk of recurrence of defects, etc., to help the construction party reasonably arrange the repair work and improve the quality and efficiency of the repair.

[0118] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for monitoring desert sand concrete construction based on data analysis is implemented.

[0119] Reference Figure 3 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call logic instructions in the memory 630 to execute a method for monitoring desert sand concrete construction based on data analysis.

[0120] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0121] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a desert sand concrete construction monitoring method based on data analysis.

[0122] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a desert sand concrete construction monitoring method based on data analysis.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring desert sand concrete construction based on data analysis, characterized in that: The method comprises the following steps: Collecting attribute characteristic information of desert sand concrete of the target construction project, and selecting a first reference construction project set and a second reference construction project from historical construction projects of the same category based on the attribute characteristic information; Extract the concrete surface defect distribution structure during the construction process of the first reference construction project set, and process the defect distribution development trend of different concrete surface defect distribution structures to obtain the defect distribution optimization trend characteristics; Extracting defect repair management information of the second reference construction project during the defect repair process, comparing and statistically analyzing the repair management development trends of different defect repair management information to obtain repair management optimization trend characteristics; After processing and analyzing the environmental impacts of the first reference construction project set, the second reference construction project, and the target construction project, a first environmental change data set and a second environmental change data set are obtained; A defect distribution monitoring result of the target construction project is obtained by performing a comprehensive evaluation based on the defect distribution optimization trend characteristics of the first environmental change data set and the first reference construction project set; A comprehensive evaluation is conducted based on the second environmental change data set and the repair management optimization trend characteristics of the second reference construction project to obtain the defect repair management results of the target construction project.

2. A method for monitoring desert sand concrete construction based on data analysis according to claim 1, characterized in that: The first environmental change data set and the second environmental change data set are obtained by processing and analyzing the environmental impacts of the first reference construction project set, the second reference construction project, and the target construction project, specifically comprising the following steps: Performing an environmental impact assessment on the first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on the second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on the target construction project to obtain a target environmental correlation impact set; The target environment association impact degree set and the first environment association impact degree set are changed by calculating the change ratio value to obtain the first environment change data set, and the target environment association impact degree set and the second environment association impact degree set are changed by calculating the change ratio value to obtain the second environment change data set.

3. A method for monitoring desert sand concrete construction based on data analysis according to claim 2, characterized in that: The attribute characteristic information includes desert sand content, desert sand strength grade, and construction process type.

4. A method for monitoring desert sand concrete construction based on data analysis according to claim 3, characterized in that: According to the attribute feature information, the first reference construction project set and the second reference construction project are selected from the historical construction projects of the same category. The following steps are involved: Collect at least two historical construction projects with the same construction environment and the same category based on attribute feature information; The concrete surface defect distribution results of each historical construction project are statistically analyzed to obtain a defect distribution statistical result set, and the corresponding historical construction project with the largest number of identical defect distribution conditions in the defect distribution statistical result set is marked as a first reference construction project set; Statistically analyzing the optimization results of the centralized defect repair management of the first reference construction project to obtain a repair management statistical result set; The historical construction project corresponding to the best defect repair management optimization result in the repair management statistical result set is marked as the second reference construction project.

5. The method for monitoring desert sand concrete construction based on data analysis according to claim 4 is characterized in that: After processing the defect distribution development trend of different concrete surface defect distribution structures, the defect distribution optimization trend characteristics are obtained, which specifically includes the following steps: Extracting defect detection status data of one concrete surface defect distribution structure and marking it as a first defect status detection value, extracting defect detection status data of another concrete surface defect distribution structure and marking it as a second defect status detection value; Calculating the difference between the first defect condition detection value and the second defect condition detection value to obtain a defect detection difference; The difference between one concrete surface defect distribution structure and another concrete surface defect distribution structure is extracted to obtain a differential defect distribution structure; wherein the differential defect distribution structure and the defect detection difference are combined to form a defect distribution optimization trend feature.

6. The method for monitoring desert sand concrete construction based on data analysis according to claim 5, characterized in that: Comparing and statistically analyzing the repair management development trends of different defect repair management information, we can obtain the repair management optimization trend characteristics, which specifically includes the following steps: Extracting defect repair completion time data of one type of defect repair management information and marking it as a first defect repair completion value; extracting defect repair completion time data of another type of defect repair management information and marking it as a second defect repair completion value; Calculate the difference between the first defect repair completion value and the second defect repair completion value to obtain a defect repair time difference; marking different repair processes for defects of the same type in one defect repair management information and another defect repair management information as different repair processes; Among them, the defect repair time difference and the difference repair process mark are repair management optimization trend features.

7. The method for monitoring desert sand concrete construction based on data analysis according to claim 6, characterized in that: Performing an environmental impact assessment on a first reference construction project set to obtain a first environmental correlation impact set, performing an environmental impact assessment on a second reference construction project to obtain a second environmental correlation impact set, and performing an environmental impact assessment on a target construction project to obtain a target environmental correlation impact set specifically includes the following steps: Obtaining environmental impact characteristic information for the first reference construction project set, the second reference construction project, and the target construction project; obtaining value indicators of various monitoring indicators in the first reference construction project set, the second reference construction project, and the target construction project; wherein the environmental impact characteristic information includes ambient temperature, humidity, and wind speed, and the value indicators include the curing speed of desert sand concrete; After evaluating the correlation impact degree between the environmental impact characteristic information and the value indicators in the first reference construction project set, a first environmental correlation impact degree set is obtained; After evaluating the correlation impact degree between the environmental impact characteristic information and the value index in the second reference construction project, a second environmental correlation impact degree set is obtained; After evaluating the correlation impact degree between the environmental impact characteristic information and value indicators in the target construction project, the target environmental correlation impact degree set is obtained.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the desert sand concrete construction monitoring method based on data analysis as described in any one of claims 1 to 7 is implemented.

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